I'm a researcher at FAIR (Facebook AI Research). I'm primarily interested in machine learning theory / science of deep learning, although I've worked on applying machine learning to various scientific problems.
Previously, I received my B.S. at Caltech in math and computer science, where I was fortunate enough to work with Yisong Yue, Yaser Abu-Mostafa, and Ashish Mahabal.
Email: {firstname}{lastname}99 at gmail dot com
Please reach out if you'd like to collaborate / have any questions about my work!
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models. [arXiv].
Kushal Tirumala*, Aram H. Markosyan*, Luke Zettlemoyer, Armen Aghajanyan
NeurIPS 2022 (Oral presentation, top 2% of accepted papers)
Investigating Generalization by Controlling Normalized Margin. [arXiv].
Alexander Farhang, Jeremy Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue
ICML 2022
Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks. [arXiv].
Tristan Thrush, Kushal Tirumala, Anmol Gupta, Max Bartolo, Pedro Rodriguez, Tariq Kane, William Gaviria Rojas, Peter Mattson, Adina Williams, Douwe Kiela
ACL 2022 System Demos
A Method for Finding Anomalous Astronomical Light Curves and their Analogues. [arXiv][package].
J Rafael MartÃnez-Galarza, Federica B Bianco, Dennis Crake, Kushal Tirumala, Ashish A Mahabal, Matthew J Graham, Daniel Giles
MNRAS 2021
A Granular Method for Finding Anomalous Light Curves and their Analogs. [pdf]
Kushal Tirumala, J Rafael MartÃnez-Galarza, Federica B Bianco, Dennis Crake, Ashish A Mahabal, Matthew J Graham, Daniel Giles
NeurIPS ML4PS workshop 2021
DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning. [pdf]
Dmitry A Duev, Ashish Mahabal, Quanzhi Ye, Kushal Tirumala, Justin Belicki, Richard Dekany, Sara Frederick, Matthew J Graham, Russ R Laher, Frank J Masci, Thomas A Prince, Reed Riddle, Philippe Rosnet, Maayane T Soumagnac
MNRAS 2019
Generalization Bounds for MLPs. [pdf]
Kushal Tirumala